Traditional talent identification relies on physical performance tests to select athletes. As players gain experience, the sensitivity of these tests in differentiating between player ability is reduced which can lead to an over-reliance on qualitative parameters and subjective selection. Using rugby union as an example, and using sample test results from the literature, this theoretical paper builds on related work by adding a number of sport-specific predictor variables (relative age, genetics, psychology and situational awareness) applied with weighting constants to forecast performance. By normalising and amalgamating test scores a single talent index was achieved and an iterative technique to fine-tune the variables and weightings proposed. The paper demonstrates how to differentiate player talent in-practice, when for example based on sprint results the sub-elite players appear to out-class the elite. This hypothetical algorithm could be used to highlight quantitative differences and better rank elite and sub-elite players in a variety of scenarios.
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